首页|融合SVM-LDA与加权相似度的潜在新兴技术识别研究——以人工智能领域为例

融合SVM-LDA与加权相似度的潜在新兴技术识别研究——以人工智能领域为例

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在新一轮科技革命和产业变革加速发展的大背景下,如何在新技术不断涌现的技术大海中精准找到和识别出有颠覆性潜力的新兴技术,对于国家、企业参与主体和相关商业投资机构把握科技创新发展趋势和方向、合理配置科技资源、提前进行科技战略规划与技术布局具有重要的意义.本文提出一种基于知识增强SVM-LDA(Support Vec-tor Machine-Latent Dirichlet Allocation)的新兴技术主题识别模型.首先,基于专家小组的先验知识,制定基础技术类别划分标准;其次,将技术类别划分标准作为先验知识输入SVM-LDA模型,得到技术主题聚类结果;再其次,基于类别主题词的加权相似度计算,确定潜在新兴关键技术;最后,以人工智能领域为例进行实证研究.采用本文模型共得到24项潜在新兴技术,主要分布在特种机器人技术、监测预警技术、视频图像处理技术、语音识别技术、自动规划和决策技术以及自然语言处理技术6个大类方向.
Identification of Potential Emerging Technologies by Fusing SVM-LDA and Weighted Similarity:Taking the Field of Artificial Intelligence as an Example
In the context of a new round of technological revolution and accelerated industrial transformation,accurately identifying emerging technologies with disruptive potential in the constantly emerging technological ocean is of great sig-nificance for the nation,enterprise participants,and relevant commercial investment institutions.It is therefore important to grasp the development trends and directions of technological innovation,reasonably allocate scientific and technological resources,and carry out advance scientific and technological strategic planning and technological layout.This article pro-poses an emerging technology topic recognition model based on knowledge-enhanced SVM-LDA.First,a classification standard for basic technology was developed based on the prior knowledge of the expert group;second,the technology cat-egory classification criteria were input into the SVM-LDA model as a priori knowledge to obtain the technology topic clus-tering results;third,a weighted similarity calculation based on category subject terms was performed to identify potential emerging key technologies;and fourth,an empirical study was conducted using the field of artificial intelligence as an ex-ample.Finally,24 potential emerging technologies were obtained,mainly distributed across six major categories:special robot technology,monitoring and early warning technology,video and image processing technology,voice recognition technology,automated planning and decision-making techniques,and natural language processing technology.

emerging technologiesknowledge-enhancedSVM-LDA modelweighted similarityAI field

冉从敬、田文芳

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武汉大学信息管理学院,武汉 430072

新兴技术 知识增强 SVM-LDA模型 加权相似度 人工智能领域

国家自然科学基金面上项目

72274084

2024

情报学报
中国科学技术情报学会 中国科学技术信息研究所

情报学报

CSTPCDCSSCICHSSCD北大核心
影响因子:1.296
ISSN:1000-0135
年,卷(期):2024.43(5)
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